Empathy gaps for social pain: Why people underestimate the pain of social suffering.
Bibliographic record
Abstract
In 5 studies, the authors examined the hypothesis that people have systematically distorted beliefs about the pain of social suffering. By integrating research on empathy gaps for physical pain (Loewenstein, 1996) with social pain theory (MacDonald & Leary, 2005), the authors generated the hypothesis that people generally underestimate the severity of social pain (ostracism, shame, etc.)--a biased judgment that is only corrected when people actively experience social pain for themselves. Using a social exclusion manipulation, Studies 1-4 found that nonexcluded participants consistently underestimated the severity of social pain compared with excluded participants, who had a heightened appreciation for social pain. This empathy gap for social pain occurred when participants evaluated both the pain of others (interpersonal empathy gap) as well as the pain participants themselves experienced in the past (intrapersonal empathy gap). The authors argue that beliefs about social pain are important because they govern how people react to socially distressing events. In Study 5, middle school teachers were asked to evaluate policies regarding emotional bullying at school. This revealed that actively experiencing social pain heightened the estimated pain of emotional bullying, which in turn led teachers to recommend both more comprehensive treatment for bullied students and greater punishment for students who bully.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".